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IBM Machine Learning Launch: February 15th (NYC)


IBM Machine Learning Launch


Watch theCUBE LIVE at IBM Machine Learning Launch 2017



“Discover best practices across data, analytics and open source projects. Hear about key new technologies and our latest offerings and solutions. This is your opportunity to gain insights on how to define the infrastructure needed to drive your current and future business requirements.” (www.ibm.com)


Event Details:

  • Date:
        February 15th, 2017
  • Location:
         Waldorf Astoria
         301 Park Avenue,
         Manhattan, NYC, New York

Wrap Up
Wrap Up – IBM Machine Learning Launch
Dave Vellante and Stu Miniman wrap up coverage at the IBM Machine Learning Launch Event at the Waldorf Astoria Hotel in New York, New York.


Guests Included


Steve Astorino
VP development, IBM private cloud analytics platform

Steve Astorino

Interview Overview: Grasping the business applications of machine learning
Steve Astorino, vice president of development, private cloud platform and z Analytics at IBM Canada, stopped by theCUBE and talked with Dave Vellante (@dvellante) and Stu Miniman (@stu) about what IBM Corp.’s opening of Watson’s core machine learning component means for businesses. As Astorino noted, a big part of the benefit comes from how the new tech allows machine learning to be automated,” making it much easier for data scientists and businesses to work together. Asked whether it was simply a new presentation for the same Watson technology, Astorino stated, “Watson is our [public] cloud solution … We’re [now] building something on private cloud for private cloud customers.” Read the full blog post with highlights from his interview at SiliconANGLE.com.
 


Barry Baker
Vice President, Offering Management for z Systems & LinuxONE, IBM Systems

Barry Baker

Interview Overview: Mainframe revival: IBM refreshes legacy business with machine learning, Linux
Barry Baker, vice president of offering management for z Systems and LinuxONE at IBM, stopped by theCUBE and talked with Dave Vellante (@dvellante) and Stu Miniman (@stu) about the massive computers run banking systems, weave the financial webs that hold nations together and control infrastructure at every level and how they need to be modernized. The conversation opened as Baker described a use case for big data on a mainframe. The bulk of the data needs to be on the platform for it to make sense to run the workload there, he stated. While the data companies want to perform machine learning on is resident on the mainframe, there is other data out there. It’s about taking a filtered subset of that data and running analytics where it makes sense, he continued. Read the full blog post with highlights from his interview at SiliconANGLE.com.
 


Jeff Josten
IBM Distinguished Engineer, DB2 for z/OS Development IBM Analytics

Jeff Josten

Interview Overview: Can a smarter mainframe with baked-in analytics solve IoT’s scale problem?
Jeff Josten, IBM distinguished engineer, DB2 for z/OS Development, IBM Analytics, platform development, stopped by theCUBE and talked with Dave Vellante (@dvellante) and Stu Miniman (@stu) about how Storing massive data is a big enough challenge for enterprises, but jetting it around for analytics is an even mightier feat. Josten said along with transaction rates and table and object sizes, they have also supersized the ingest rates. “We increased the ingest rates so that we can allow for inserts into a single table. We achieved over 11 million inserts per second, fully recoverable, fully logged inserts,” he said. Read the full blog post with highlights from his interview at SiliconANGLE.com.
 


James Kobielus
Senior Program Director, Community Engagement & Strategy, Data Science
IBM Analytics

James Kobielus

Interview Overview: Machine learning beyond data scientists: the self-serve model emerges
James Kobielus, senior program director of product marketing and big data analytics, IBM Analytics, at IBM Corp, stopped by theCUBE and talked with Dave Vellante (@dvellante) and Stu Miniman (@stu) about how machine learning has recently come into its own because of the potential for applied automation in finding patterns and correlations in data sets. While machine learning has been around for a number of years in the form of artificial neural networks, recent developments have commercialized and refined the tools of machine learning to a much greater degree, making them far more useful to data scientists. Today’s launch for IBM‘s Machine Learning platform, delivered first to IBM’s z System mainframes, will greatly help data scientists automate and process the data that they need, Kobielus explained. Read the full blog post with highlights from his interview at SiliconANGLE.com.
 


Dinesh Nirmal
Vice President, Analytics Development, IBM Analytics

Dinesh Nirmal

Interview Overview: Deploying a machine learning model with Watson’s newly exposed tools
Dinesh Nirmal, vice president of analytics development, IBM Analytics, at IBM, stopped by theCUBE and talked with Dave Vellante (@dvellante) and Stu Miniman (@stu) about how IBM is evolving machine learning models and predictive analytics. With its machine learning platform, IBM is bringing in flexibility so that customers can use whatever software language or execution engine they prefer. The platform also contains a new collaborative piece to facilitate productivity between organizations, Nirmal stated. Read the full blog post with highlights from his interview at SiliconANGLE.com.
 


Jean Francois Puget
DE, Machine Learning and Optimization, IBM Analytics, Platform Development

Jean Francois Puget

Interview Overview: The evolution of machine learning: fusing human thought with algorithmic insights
Jean Francois Puget, distinguished engineer, machine learning and optimization, IBM Analytics, at IBM, stopped by theCUBE and talked with Dave Vellante (@dvellante) and Stu Miniman (@stu) about how as machine learning becomes more accessible through different avenues, some developers and industry insiders are cautioning against getting too dazzled by the potential without considering the human role. “For most people, machine learning equals machine learning algorithms,” Puget said. “When you look at newspapers or blogs, social media, it’s all about algorithms. Our view [is] that sure, you need algorithms for machine learning, but you need steps before you run algorithms, and after.” Read the full blog post with highlights from his interview at SiliconANGLE.com.
 


Bryan Smith
CTO, Rocket Software

Bryan Smith

Interview Overview: Rocket Software talks mainframes with data scientists who’ve never seen one
Bryan Smith, CTO at Rocket Software, stopped by theCUBE and talked with Dave Vellante (@dvellante) and Stu Miniman (@stu) about how the analytics process of ETL (extract, transform, load) is dead and that mainframes are the future. Smith said he enjoyed educating them about what a mainframe is and how it relates to the ETL issue. “ETL’s future is very bleak,” he said, explaining that the process is too slow for real-time or near real-time analytics. “It had its time, but now it’s done, because now you can access that data in place.” Read the full blog post with highlights from his interview at SiliconANGLE.com.
 


Rob Thomas
General Manager, IBM Analytics

Rob Thomas

Interview Overview: How cognitive tech can handle sensitive data in private clouds
Rob Thomas, general manager of IBM Analytics at IBM, stopped by theCUBE and talked with Dave Vellante (@dvellante) and Stu Miniman (@stu) about how IBM Corp. is bringing transactions and machine learning together to extract data’s real value. “We are telling clients that you can get the power of machine learning across any type of data, whether its data in a warehouse, a database, unstructured content, email, you name it — we are bringing machine learning everywhere,” said Thomas. Read the full blog post with highlights from his interview at SiliconANGLE.com.
 



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